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Local Llm Router

Listed
hoodini·vlatest·ai-ml

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add hoodini/ai-agents-skills --skill local-llm-router -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

hoodini

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: 192.168.1.100, ollama.ai.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    192.168.1.100undeclaredollama.aiundeclared

    What it found

    • skills/local-llm-router/SKILL.md:1757contacts 192.168.1.100
      endpoint: "http://192.168.1.100:8000"
    • skills/local-llm-router/references/model-matrix.md:245contacts ollama.ai
      curl -fsSL https://ollama.ai/install.sh | sh

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • skills/local-llm-router/references/model-matrix.md:245remote script piped into a shell
      curl -fsSL https://ollama.ai/install.sh | sh

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned hoodini/ai-agents-skills at f7a43d8f85.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
f7a43d8f85
Manifest
skills/local-llm-router/SKILL.md
Scanned
2 files, 2,796 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The local-llm-router skill enables AI agents to dynamically select and route requests to specific locally hosted Large Language Models based on defined criteria. It manages connections to multiple open-source models running on your machine without requiring external API keys or cloud dependencies.

When to use it

  • Privacy-first workflows: Process sensitive data entirely offline where internet connectivity is restricted or prohibited.
  • Cost optimization: Reduce expenses by utilizing free, self-hosted models instead of paid enterprise APIs for routine tasks.
  • Custom model testing: Quickly switch between different local architectures (e.g., Llama 3 vs. Mistral) to evaluate performance on specific datasets.
  • Low-latency requirements: Minimize response times by routing queries directly to a model running on the same network as the agent.

Key capabilities

  • Routes incoming prompts to multiple configured local LLM endpoints simultaneously.
  • Supports various open-source model formats commonly used in local environments.
  • Manages connection states and health checks for locally deployed inference servers.
  • Provides a unified interface to interact with disparate local models through a single agent skill.

Example prompts

  • "Route this legal document analysis to the high-precision local model, but keep the summary generation on the faster, smaller instance."
  • "Check if the primary local LLM is responsive; if not, automatically failover to the backup endpoint for this query."
  • "Process this batch of internal employee records using the secure, offline-only model configuration."

Tips & gotchas

Ensure your local inference server (such as Ollama or vLLM) is running and accessible via the network before attempting to route requests. Performance will vary significantly depending on your hardware specifications and the size of the selected local models.